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MOOCs enable lifelong learners from around the world to interact with one another at unprecedented scales. Early literature on MOOCs has investigated the nature of learner interactions with their course environments. However, to date we know very little about the nature of interactions between learners or how these individuals exchange information with one another. Through a mixed method analysis of two MOOCs that emphasize collaborative problem-solving efforts, we aim to better understand who interacts with who in MOOCs, and how. We plan to interpret these interactions by contextualizing them according to the demographic characteristics and academic activities of each learner. These investigations will aid in analysing the formation of crowds versus communities in discussion settings; how information is aggregated and transmitted through interaction networks; and how participant backgrounds, course activities, performance, and communication tendencies are related. Using social network analysis and Bayesian inference in conjunction with insights derived from observations, participant interviews, and surveys, we hope to uncover how interaction patterns help us to understand how learning occurs through online interactions in ways that build on existing theoretical frameworks developed from previous learning and technology research. Ultimately, we aim to use this hybrid analytical framework to develop a typology that reflects the different ways in which MOOC participants communicate and interact in order to learn.